Enterprises now track all aspects of their business electronically. Every transaction with a customer, information about the customer, inventory, capital, expenses, etc. are captured, indexed, and stored in an enterprise's database. Very quickly the enterprise's database becomes enormous in size having a plethora of information. Accordingly, enterprises are increasingly relying on their information for driving and managing all aspects of their business operations.
In fact, enterprises often develop reports and real-time statistics from their databases. Typically, the interface for achieving these reports and statistics is a Structured Query Language (SQL). Often, analysts develop complex SQL statements that execute against the database for purposes of gaining different insight into the details of the business.
These SQL statements can include a variety of nested and complex rules and may rely on results from prior SQL queries. Unfortunately, users are generally not permitted to use metric results as a source or driver to the complex rules embedded in SQL statements.
So, users may have to iterate may have to develop many different sets of SQL statements to account for results that may be needed. This is time consuming and inefficient.
As a result, improved techniques for using database metric results are needed.